Where Domain Evals Matter Most

The Two Forces

A marketing team can eyeball AI-drafted social posts. A process engineer verifying whether recommended operating parameters will cause a thermal runaway cannot. That gap is where the money lives.

Two axes define where domain experts as eval builders create the most value. Plot any domain against them and you get a clear investment thesis.

Axis 01

Penalty for Failure

What happens when the AI is wrong and nobody catches it? Ranges from mild embarrassment to regulatory fines, physical harm, or death.

Axis 02

Verification Opacity

How hard is it for a non-expert to spot errors? High opacity means the expert's judgment is irreplaceable. No generic SelfCheckGPT catches it.

The Eval Prioritization Grid

Four quadrants. One tells you where the durable moats are built.

PENALTY FOR FAILURE →

Critical

High Penalty · High Opacity

"Expert evals are existential"

Wrong answers kill people, trigger lawsuits, or cause environmental disasters. A generalist reviewer cannot tell if a dosage or SIL calculation is correct. Premium pricing. Durable moats.

Medical diagnostics · Process safety · Structural engineering · Pharma R&D

High Value

High Penalty · Low Opacity

"Evals are valuable but contestable"

Errors are expensive but often caught by existing audit processes. AI output is checkable by trained, non-specialist reviewers. Evals add speed but moat is weaker.

Financial reporting · Tax compliance · Insurance underwriting

Niche

Low Penalty · High Opacity

"Expert evals are a luxury"

Errors matter to quality but rarely create catastrophic outcomes. Experts add real value, but willingness to pay is limited — downside is reputational, not existential.

Academic research · Niche creative · Specialized translation

Generic

Low Penalty · Low Opacity

"Generic evals suffice"

Anyone can check the output. Errors are easily caught and cheaply fixed. Standard LLM-as-Judge or RAG-based verification handles it. Expert evals are overkill.

Content generation · Summarization · Internal comms

← Low opacity

VERIFICATION OPACITY

High opacity →

Domain Scoring — Five Questions

What breaks when the AI is wrong?

Equipment damage, patient harm, regulatory penalty, financial loss, or just embarrassment. Process safety scores 5. Content marketing scores 1.

1 = embarrassment · 5 = death/shutdown

Can a smart generalist spot the error?

If reviewing requires board certification, a PE license, or 10+ years of domain experience, score high. Expertise translates directly into eval quality.

1 = anyone · 5 = board-certified only

How many AI interactions happen daily?

Volume matters. 10,000 daily queries generates enough data for the eval flywheel to spin. Low-volume domains make the economics harder.

1 = dozens · 5 = 10K+ daily queries

How scarce are the domain experts?

Scarce experts mean higher eval value per person. Millions of marketers but a few thousand process safety engineers for IEC 61511 compliance. Scarcity creates pricing power.

1 = abundant · 5 = thousands globally

Does regulation mandate verification?

Regulated domains have forced buyers. Healthcare (FDA), aviation (FAA), financial services (SEC/FCA), process safety (IEC 61508). Converts "nice to have" into "required to operate."

1 = optional · 5 = legally mandated

Top Tier Niches

Domains that score 4–5 across most dimensions

Where expert-built evals command premium pricing, create structural lock-in, and compound with usage.

Score: 5/5

Clinical Medicine & Diagnostics

Highest penalty. Maximum opacity. Heavily regulated. Scarce specialists. Every AI-assisted diagnosis needs evals written by clinicians who know what a subtle presentation of sepsis looks like versus a benign fever.

Penalty 5

Opacity 5

Regulated Yes

Score: 5/5

Process Safety & Industrial Ops

$84M/facility/year in downtime costs from false alarms alone. Only experienced plant operators distinguish real hazards from sensor noise. Evals encode decades of operational intuition.

Penalty 5

Opacity 5

Volume High

Score: 4–5/5

Pharmaceutical R&D

Drug interaction checks, dosage calculations, clinical trial protocol review. Errors cascade into patient harm and billion-dollar liability. Expert pool is tiny. Eval sets become proprietary assets.

Penalty 5

Scarcity 5

Regulated Yes

Score: 4/5

Legal & Regulatory Compliance

Jurisdictional complexity creates verification opacity. A clause valid in Delaware might be unenforceable in Germany. Only practitioners with jurisdiction-specific experience write meaningful evals.

Penalty 4

Opacity 4

Regulated Yes

Score: 4/5

Financial Risk & Audit

Material misstatement triggers SEC action and shareholder lawsuits. CPAs bring verification instincts honed over thousands of engagements. Eval library maps to specific standards (GAAP, IFRS), creating structural lock-in.

Penalty 4 · Opacity 4 · Regulated Yes · Volume High

Standards-mapped eval libraries (GAAP, IFRS) create switching costs that become structural once referenced in audit workflows.

The Defensibility Stack

Individual eval questions are easy to replicate. 10,000 calibrated, expert-validated test cases are not.

The moat compounds across three layers, each reinforcing the others.

Data Flywheel

More usage generates more edge cases generates better evals. Wright's Law applied to verification quality. Each production correction feeds the next version.

→ Compounding quality advantage

Expert Network

The community of domain experts contributing evals is itself a moat. Recruiting and retaining them creates a talent barrier competitors can't buy their way past.

→ Supply-side lock-in

Regulatory Entrenchment

Once eval sets are referenced in regulatory guidance or industry standards, switching costs become structural. The eval set becomes infrastructure.

→ Demand-side switching costs